Testing hypotheses about fecundity, body size and maternal condition in fishes
Bibliographic record
Abstract
Abstract Recent research suggests that maternal condition positively influences the number of eggs spawned in fishes. These studies commonly choose a priori to use body length rather than weight as an explanatory variable of offspring production, even though weight is usually the better predictor of fecundity. We are concerned that consistent exclusion of body weight as a predictor of egg production inflates the variance in fecundity attributable to maternal condition. By analysing data on three populations of Atlantic cod (Gadus morhua, Gadidae) and 10 populations of brook trout (Salvelinus fontinalis, Salmonidae), we illustrate the need for a statistically defensible method of model selection to distinguish the effects of maternal condition on egg production from the effects of body size alone. Forward stepwise regression and null model analyses reveal how length‐based regressions can significantly over‐estimate correlations between condition and fecundity, leading us to conclude that the effect of condition on egg productivity may not be as ubiquitous or as biologically important as previously thought. Our work underscores the need for greater statistical clarity in analyses of the effects of maternal condition on reproductive productivity in fishes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".